Vibro-Acoustic Modulation Utilizing a Swept Probing Signal for Robust Crack Detection

Abstract

One practical issue that must be addressed prior to the implementation of a vibration-based structural health monitoring system is the influence that variations in the structure’s environmental and boundary conditions can have on the vibration response of the structure. This issue is especially prominent in the structural health monitoring of aircraft, which operate in a wide variety of different environmental conditions and possess complex structural components connected through various boundary conditions. However, many types of damage introduce nonlinear stiffness and damping restoring forces, which may be used to detect damage even in the midst of these varying conditions. Vibro-acoustic modulation is a nondestructive evaluation technique that is highly sensitivity to the presence of nonlinearities. One factor that complicates the use of vibro-acoustic modulation as a structural health monitoring technique is that the amount of measured modulation has been shown to be dependent on the frequency of the probing signal. The frequency dependence of the modulation was investigated and the magnitude of modulation was found to be correlated with the underlying vibration characteristics of the structure, which are influenced by environmental and boundary condition variations. To facilitate the use of nonlinear vibro-acoustics for the health monitoring of complex aerospace components in varying environments, a vibro-acoustic modulation technique utilizing a swept probing signal has been developed. The developed method was demonstrated on a steel beam in varying operational conditions. The presence of a crack in the beam was detected both through an increase in the amount of normalized modulation and without the use of historical data by utilizing generalized extreme value statistics.

Keywords

nonlinear SHM vibro-acoustic modulation nonlinear crack detection environmental variability baseline free damage detection generalized extreme value statistics.

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